IP Library Granted Patent US 12,205,367
Granted Patent B2
US 12,205,367 · App. 18/499,637 · Granted Jan 21, 2025

Devices, methods, and graphical user interfaces for analyzing, labeling, and managing land in a geospatial platform

Inventors: Pritesh Jain (Bengaluru, IN); Stephen A. Marland (Royal Leamington Spa, GB)
Assignee: AIDash, Inc.
G06V20/13G06V10/764
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,205,367
App. No.
18/499,637
Granted
Jan 21, 2025
Kind
B2
Abstract

Example systems, methods, and non-transitory computer readable media are directed to determining a geographic location to be assessed; obtaining information associated with the geographic location, the information including at least one data point of the geographic location; classifying one or more habitats within the geographic location based on at least one machine learning model that processes the at least one data point of the geographic location; determining at least one respective metric for the one or more classified habitats based at least in part on the at least one data point of the geographic location; and providing an interface that includes at least a map of the geographic location and the at least one respective metric for the one or more classified habitats, the one or more classified habitats are visually segmented in the map by habitat type.

Claims (46)

1. A system comprising:

at least one processor; and

memory, the memory containing instructions to control any number of the at least one processor to:

determine a geographic location to be assessed;

obtain information associated with the geographic location, the information including at least one data point of the geographic location;

classify one or more habitats within the geographic location based on at least one machine learning model that processes the at least one data point of the geographic location;

determine at least one respective metric for the one or more classified habitats based at least in part on the at least one data point of the geographic location;

provide an interface that includes at least a map of the geographic location and the at least one respective metric for the one or more classified habitats, wherein the one or more classified habitats are visually segmented in the map by habitat type;

receive a selection of a region of the one or more classified habitats, wherein the region is designated for land conversion;

determine at least one change to the at least one respective metric for the region based upon the land conversion; and

provide information describing the at least one change to the at least one respective metric in the interface.

2. The system of claim 1 wherein the interface provides an option to digitally draw physical boundaries of the region within the map of the geographic location.

3. The system of claim 1 wherein the interface provides an option to specify new attributes for the region designated for land conversion, the new attributes including at least a new habitat type planned for the region.

4. The system of claim 1 wherein the at least one change to the at least one respective metric for the region include one or more of: a change to a biodiversity measurement associated with the geographic location, a change to a carbon measurement associated with the geographic location, a change to an air quality measurement associated with the geographic location, a change to a flood risk measurement associated with the geographic location, a change to a timber measurement associated with the geographic location, a change to a food production measurement associated with the geographic location, a change to a pollination measurement associated with the geographic location, a change to a natural capital measurement associated with the geographic location, a change to a monetary value measurement associated with the geographic location, a change to a societal value measurement associated with the geographic location, and a change to a tree measurement associated with the geographic location.

5. The system of claim 1 wherein the instructions further control any number of the at least one processor to provide read-only access to the interface to at least one third-party tasked with approving the land conversion.

6. The system of claim 1 wherein the at least one respective metric for the one or more classified habitats is determined based on a user-defined configuration that specifies one or more assessments to be performed in relation to the geographic location.

7. The system of claim 1 , wherein the at least one respective metric for the one or more classified habitats corresponds to a biodiversity metric, a carbon metric, an air quality metric, a flood risk metric, a timber metric, a food production metric, a pollination metric, a natural capital metric, a monetary value metric, a societal value metric, a tree count metric, or a tree height metric.

8. A non-transitory computer-readable medium comprising executable instructions, the executable instructions being executable by one or more processors to perform a method, the method comprising:

determining a geographic location to be assessed;

obtaining information associated with the geographic location, the information including at least one data point of the geographic location;

classifying one or more habitats within the geographic location based on at least one machine learning model that processes the at least one data point of the geographic location;

determining at least one respective metric for the one or more classified habitats based at least in part on the at least one data point of the geographic location;

providing an interface that includes at least a map of the geographic location and the at least one respective metric for the one or more classified habitats, wherein the one or more classified habitats are visually segmented in the map by habitat type;

receiving a selection of a region of the one or more classified habitats, wherein the region is designated for land conversion;

determining at least one change to the at least one respective metric for the region based upon the land conversion; and

providing information describing the at least one change to the at least one respective metric in the interface.

9. The non-transitory computer-readable medium of claim 8 wherein the interface provides an option to digitally draw physical boundaries of the region within the map of the geographic location.

10. The non-transitory computer-readable medium of claim 8 wherein the interface provides an option to specify new attributes for the region designated for land conversion, the new attributes including at least a new habitat type planned for the region.

11. The non-transitory computer-readable medium of claim 8 wherein the at least one change to the at least one respective metric for the region include one or more of: a change to a biodiversity measurement associated with the geographic location, a change to a carbon measurement associated with the geographic location, a change to an air quality measurement associated with the geographic location, a change to a flood risk measurement associated with the geographic location, a change to a timber measurement associated with the geographic location, a change to a food production measurement associated with the geographic location, a change to a pollination measurement associated with the geographic location, a change to a natural capital measurement associated with the geographic location, a change to a monetary value measurement associated with the geographic location, a change to a societal value measurement associated with the geographic location, and a change to a tree measurement associated with the geographic location.

12. The non-transitory computer-readable medium of claim 8 wherein the method further comprises providing read-only access to the interface to at least one third-party tasked with approving the land conversion.

13. The non-transitory computer-readable medium of claim 8 wherein the at least one respective metric for the one or more classified habitats is determined based on a user-defined configuration that specifies one or more assessments to be performed in relation to the geographic location.

14. The non-transitory computer-readable medium of claim 8 wherein the at least one respective metric for the one or more classified habitats corresponds to a biodiversity metric, a carbon metric, an air quality metric, a flood risk metric, a timber metric, a food production metric, a pollination metric, a natural capital metric, a monetary value metric, a societal value metric, a tree count metric, or a tree height metric.

15. A method comprising:

determining a geographic location to be assessed;

obtaining information associated with the geographic location, the information including at least one data point of the geographic location;

classifying one or more habitats within the geographic location based on at least one machine learning model that processes the at least one data point of the geographic location;

determining at least one respective metric for the one or more classified habitats based at least in part on the at least one data point of the geographic location;

providing an interface that includes at least a map of the geographic location and the at least one respective metric for the one or more classified habitats, wherein the one or more classified habitats are visually segmented in the map by habitat type;

receiving a selection of a region of the one or more classified habitats, wherein the region is designated for land conversion;

determining at least one change to the at least one respective metric for the region based upon the land conversion; and

providing information describing the at least one change to the at least one respective metric in the interface.

16. The method of claim 15 wherein the interface provides an option to digitally draw physical boundaries of the region within the map of the geographic location.

17. The method of claim 15 wherein the interface provides an option to specify new attributes for the region designated for land conversion, the new attributes including at least a new habitat type planned for the region.

18. The method of claim 15 wherein the at least one change to the at least one respective metric for the region include one or more of: a change to a biodiversity measurement associated with the geographic location, a change to a carbon measurement associated with the geographic location, a change to an air quality measurement associated with the geographic location, a change to a flood risk measurement associated with the geographic location, a change to a timber measurement associated with the geographic location, a change to a food production measurement associated with the geographic location, a change to a pollination measurement associated with the geographic location, a change to a natural capital measurement associated with the geographic location, a change to a monetary value measurement associated with the geographic location, a change to a societal value measurement associated with the geographic location, and a change to a tree measurement associated with the geographic location.

19. The method of claim 15 , further comprising providing read-only access to the interface to at least one third-party tasked with approving the land conversion.

20. The method of claim 15 wherein the at least one respective metric for the one or more classified habitats is determined based on a user-defined configuration that specifies one or more assessments to be performed in relation to the geographic location.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 19, 2023
From: JAIN, PRITESH; MARLAND, STEPHEN A.
To: AIDASH INC.
Reel/Frame 065916/0715 →
Continuity (2)
Continuation 18149036 · Dec 30, 2022
Related Publication 20240221376A1 · Jul 4, 2024
References Cited (30)
US 11074447B1 · Fox et al. · 2021 [cited by applicant]
US 11120692B2 · Aljuaid et al. · 2021 [cited by applicant]
US 11126170B2 · Wilhelm et al. · 2021 [cited by applicant]
US 11132102B2 · Kornmann et al. · 2021 [cited by applicant]
US 11132377B2 · Hong · 2021 [cited by applicant]
US 11842537B1 · Jain et al. · 2023 [cited by applicant]
US 20070291994A1 · Kelle et al. · 2007 [cited by applicant]
US 20100040260A1 · Kelle et al. · 2010 [cited by applicant]
US 20100250199A1 · Breedlove · 2010 [cited by applicant]
US 20110122138A1 · Schmidt et al. · 2011 [cited by applicant]
US 20150294154A1 · Sant et al. · 2015 [cited by applicant]
US 20180330435A1 · Garg · 2018 [cited by applicant]
US 20200117959A1 · Sargent · 2020 [cited by examiner]
US 20200242754A1 · Peters · 2020 [cited by applicant]
US 20220405870A1 · Conway et al. · 2022 [cited by applicant]
Abdi, “Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data,” GIScience & Remote Sensing, vol. 57, Aug. 22, 2019, DOI: 10.1080/15481603.2019.16504… [cited by applicant]
Forsmoo, J., Anderson, K., MacLeod, C. J. A., Wilkinson, M. E., and Brazier, R., Nov. 2018., en“Drone-based structure-from-motion photogrammetry captures grassland sward height variability,” Journal of Applied Ecology 5… [cited by applicant]
International Application No. PCT/US2023/086112, International Search Report and the Written Opinion, Mar. 1, 2024, 10 pages. [cited by applicant]
Karila, K., Alves Oliveira, R., Ek, J., Kaivosoja, J., Koivumäki, N., Korhonen, P., Niemeläinen, O., Nyholm, L., Näsi, R., Pölönen, I., and Honkavaara, E., Jun. 2022., en“Estimating Grass Sward Quality and Quantity Para… [cited by applicant]
Nguyen, C. T., Chidthaisong, A., Kieu Diem, P., and Huo, L.-Z., Feb. 2021., en“A Modi-fied Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8,” Land 10, 23… [cited by applicant]
Nickmilder, C., Tedde, A., Dufrasne, I., Lessire, F., Tychon, B., Curnel, Y., Bindelle, J., and Soyeurt, H., Jan. 2021., en“Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures B… [cited by applicant]
Oliveira, R. A., Näsi, R., Niemeläinen, O., Nyholm, L., Alhonoja, K., Kaivosoja, J., Jauhi-Ainen, L., Viljanen, N., Nezami, S., Markelin, L., Hakala, T., and Honkavaara, E., Sep. 2020., en“Machine learning estimators fo… [cited by applicant]
Padalia, H., and Musthafa, M., Feb. 2017., en“Characterization and classification of fresh-water marshy wetland using synthetic aperture radar polarimetry: a case study from Loktak wetland, Northeast India,” Journal of … [cited by applicant]
Pratomoatmojo, “LanduseSim Resources Centre”, Sep. 2017,http://www.landusesim.com/resources/. [cited by applicant]
Sikder, “Geospatial Web Services in environmental planning,” 2008 11th International Conference on Computer and Information Technology, pp. 424-429, Dec. 2008. [cited by applicant]
Stewart, K., Bourn, N., and Thomas, J., Oct. 2001., en“An evaluation of three quick meth-ods commonly used to assess sward height in ecology: Sward height measurement,” Journal of Applied Ecology 38, 1148-1154. [cited by applicant]
Talukdar et al., “Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations: A Review,” Remote Sensing, Apr. 2, 2020. DOI: 10.3390/rsl2071135. [cited by applicant]
UK Patent application No. 2311213.9, Search and Examination Report dated Feb. 1, 2024, 9 pages. [cited by applicant]
Wang, Junye, et al., “Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects,” Science of The Total Environment, vol. 822, May 20, 2022. DOI: 0.1016/j.scitotenv.20… [cited by applicant]
UK Patent application No. 2407265.4, Search and Examination Report dated Sep. 11, 2024, 7 pages. [cited by applicant]